Getting Started With Data Science
Getting Started With Data Science

Author: Murtaza Haider
Publisher: IBM Press
Pages: 608
ISBN: 978-0133991024
Print: 0133991024
Kindle: B019D322UU
Audience: IT people who need to analyse data
Rating: 4.5
Reviewer: Kay Ewbank

This book aims to make data analytics more accessible by using interesting examples. 

If you've enjoyed books such as Freakonomics or Outliers, you'll feel at home reading this book as it uses a similar approach; take an interesting question such as 'Does the higher price of cigarettes deter smoking?', and use that as the basis for some data analysis.

The aim is to teach you how to do your own analyses. Haider works through the examples in R, Stata, SPSS and SAS. Within the book the examples are worked mainly in R, and one of the other languages. The code for the other languages is available for download from the IBM Press website, along with details of how to use it. 

 

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The book opens with a chapter called 'the bazaar of storytellers' that discusses what data science is and gives the author's definition of a data scientist. The next chapter, data in the 24/7 connected world, identifies sources of data that you can analyse, and also introduces the concept of big data. Chapter three looks at how data becomes meaningful when it is used as the basis for 'stories'. Haider's view is that the strength of data science lies in the power of the narrative, and that is what underpins most of the book.

From a practical perspective, the book begins to get useful in chapter four,  which looks at how you can generate summary tables, including multi-dimensional tables. Next is a chapter on graphics and how to generate them. If you're thinking that it seems a bit odd to concentrate on the 'end result' first, you have to remember that the author's view is that data analysis is only useful if your audience actually looks at the results and understands them.

 

 

The next chapter gets more into the workings of data analysis with an examination of hypothesis testing using techniques such as t-tests and correlation analysis. Regression analysis is looked at next, based on the notions "why tall parents don't have even taller children". This is a fun chapter, with examples including consumer spending on food and alcohol, housing markets, and whether the appearance of teachers affects their evaluations by students.

A chapter on analysis of binary variables considers logit and probit models using data from New York transit use. Categorical data and multinomial variables are the topic of the next chapter, which expands on the ideas of logit models.

Spatial data analysis is covered next, taking us into the use of GIS systems and how these have expanded the options for data analysis. There's a good chapter on time series analysis looking at how regression models can be used with time series data, using the examples of forecasting housing markets.

The final chapter introduces the field of data mining. It's more of a taster discussing some of the techniques that can be used, but fun anyway.

Overall, this is a book that is accessible, interesting and still manages to introduce the statistical techniques you need to use for real data analytical work. A good way to get into data analysis. 

Related Reviews

Data Science and Big Data Analytics

Doing Data Science

R in Action: Data Analysis and Graphics with R (2e)

Learning To Love Data Science 

 

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The Theoretical Minimum

Author: Leonard Susskind & George Hrabovsky
Publisher: Basic Books/Allen Lane
Pages: 256
ISBN: 978-0465028115 
Print: 0465075681
Kindle: B00B05XGSW    

Audience: Readers with solid background in Physics and Math
Rating: 5
Reviewer: Mike James

 

Want to really unders [ ... ]



Python In Easy Steps

Author: Mike McGrath
Publisher: Easy Steps
Pages: 192
ISBN: 978-1840785969
Audience: Complete beginners to programming
Rating: 3
Reviewer: Sue Gee

Python has become a very popular programming language for complete beginners. Does this book give you a firm foundation?


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Last Updated ( Tuesday, 26 July 2016 )
 
 

   
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